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formant_functions.r
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formant_functions.r
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###########################################################################
# formant_functions.r revised Jun 29, 2015
# Jeff Mielke
# functions for working with formant measurements
###########################################################################
require(car)
require(gplots)
require(plyr)
require(dtt)
# CLASSES OF SOUNDS #######################################################
word_boundary <- '#'
low_vowels <- c('a','ɑ̃')
plain_mid_vowels <- c('ə','œ','œ̃','ø','ɛ','e','ɔ','o','ɛ̃','ɔ̃')
diphthongal_mid_vowels <- c('ɛʁ')
mid_vowels <- c(plain_mid_vowels, diphthongal_mid_vowels)
tense_high_vowels <- c('i','y','u')
lax_high_vowels <- c('iC','yC','uC')
high_vowels <- c(tense_high_vowels, lax_high_vowels)
vowels <- c(high_vowels, mid_vowels, low_vowels)
oral_vowels <- c('i','iC','y','yC','e','ø','ɛ','ɛʁ','œ','ə','a','ɔ','o','uC','u')
nasal_vowels <- c('ɛ̃','œ̃','ɑ̃','ɔ̃')
french_vowels_sorted <- c('i','iC','y','yC','e','ø','ɛ','ɛ̃','ɛʁ','œ','œ̃','ə','a', 'ɑ̃','ɔ̃','ɔ','o','uC','u') #, 'ɑ'
french_approximants <- c('i','iC','j','y','yC','ɥ','e','ø','ɛ','ɛ̃','ɛʁ','œ','œ̃','ə','a', 'ɑ', 'ɑ̃','ɔ̃','ɔ','o','uC','u','w')
nasals <- c('n','m')
approximants <- c('l','j','w')
stops <- c('d','t','k','p','b','g')
voiceless_fricatives <- c('s','f','ʃ')
voiced_fricatives_not_r <- c('ʒ','v','z')
voiced_fricatives <- c(voiced_fricatives_not_r,'ʁ')
laxing_triggers <- c(nasals, approximants, stops, voiceless_fricatives)
consonants <- c(laxing_triggers, voiced_fricatives)
consonants_not_s_or_r <- setdiff(consonants, c('s', 'ʁ'))
consonants_not_approx <- setdiff(consonants, c(approximants, 'ʁ'))
p2fa_vowels <- c('IY1', 'IY2', 'IY0', 'IH1', 'IH2', 'IH0', 'IR1', 'IR2', 'IR0',
'EY1', 'EY2', 'EY0', 'EYR1', 'EYR2', 'EYR0',
'EH1', 'EH2', 'EH0', 'AH1', 'AH2', 'AH0',
'AE1', 'AE2', 'AE0', 'AY1', 'AY2', 'AY0', 'AW1', 'AW2', 'AW0', 'AA1', 'AA2', 'AA0', 'AAR1', 'AAR2', 'AAR0',
'AO1', 'AO2', 'AO0', 'OW1', 'OW2', 'OW0', 'OY1', 'OY2', 'OY0', 'OR1', 'OR2', 'OR0', 'ER1', 'ER2', 'ER0',
'UH1', 'UH2', 'UH0', 'UW1', 'UW2', 'UW0', 'UR1', 'UR2', 'UR0')
p2fa_stressed_vowels <- c('IY1', 'IH1', 'EY1', 'EH1', 'AH1', 'AE1', 'AA1', 'AO1', 'OW1', 'ER1', 'UH1', 'UW1')
p2fa_stressed_vowels_plus <- c('IY1', 'IH1', 'EY1', 'EH1', 'AH1', 'AE1', 'AY1', 'AW1', 'AA1', 'AO1', 'OW1', 'OY1', 'ER1', 'UH1', 'UW1',
'L', 'R', 'W', 'Y')
#p2fa_stressed_vowels_and_liquids <- c(p2fa_stressed_vowels, 'L', 'R')
p2fa_stressed_vowels_and_liquids <- c('IY1', 'IYN1', 'IYL1', 'IYR1', 'IH1', 'IHN1', 'IHL1', 'IHR1',
'EY1', 'EYN1', 'EYL1', 'EYR1', 'EH1', 'EHN1', 'EHL1', 'EHR1',
'AH1', 'AHN1', 'AHL1', 'AHR1', 'AE1', 'AEN1', 'AEL1', 'AER1',
'AA1', 'AAN1', 'AAL1', 'AAR1', 'AY1', 'AYN1', 'AYL1', 'AYR1',
'AW1', 'AWN1', 'AWL1', 'AWR1', 'AO1', 'AON1', 'AOL1', 'AOR1',
'OW1', 'OWN1', 'OWL1', 'OWR1', 'ER1', 'UH1', 'UHN1', 'UHL1', 'UHR1',
'UW1', 'UWN1', 'UWL1', 'UWR1', 'L', 'R')
buckeye_basic_vowels <- c('iy', 'ih', 'ey', 'eh', 'ah', 'ae', 'aa', 'ao', 'ow', 'el', 'er', 'uh', 'uw')
buckeye_oral_vowels <- c('iy', 'ih', 'ey', 'eh', 'ah', 'ae', 'ay', 'aw', 'aa', 'ao', 'ow', 'oy', 'el', 'er', 'uh', 'uw')
buckeye_nasal_vowels <- c('iyn','ihn','eyn','ehn','ahn','aen','ayn','awn','aan','aon','own','oyn','eln','ern','uhn','uwn')
buckeye_vowels <- c(buckeye_oral_vowels)
all_vowels <- c(p2fa_vowels, oral_vowels, nasal_vowels, buckeye_vowels, buckeye_nasal_vowels)
p2fa_coronals <- c('S', 'SH', 'N', 'T', 'Z', 'D', 'TH', 'DH', 'CH', 'JH', 'ZH')
p2fa_consonants <- c('K', 'S', 'M', 'SH', 'N', 'P', 'T', 'Z', 'D', 'B', 'V', 'NG', 'G', 'TH', 'F', 'DH', 'HH', 'CH', 'JH', 'ZH')
p2fa_approximants <- c('L', 'W', 'R', 'Y')
p2fa_vplus <- c('Y', 'IY1', 'IY2', 'IY0', 'IH1', 'IH2', 'IH0',
'EY1', 'EY2', 'EY0', 'EH1', 'EH2', 'EH0',
'AE1', 'AE2', 'AE0',
'AH1', 'AH2', 'AH0', 'ER1', 'ER2', 'ER0', 'R',
'AY1', 'AY2', 'AY0', 'AW1', 'AW2', 'AW0',
'AA1', 'AA2', 'AA0', 'AO1', 'AO2', 'AO0',
'OY1', 'OY2', 'OY0', 'OW1', 'OW2', 'OW0',
'UH1', 'UH2', 'UH0', 'UW1', 'UW2', 'UW0', 'W', 'L')
p2fa_vl <- c('Y',
'IY1', 'IY2', 'IY0', 'IY1R', 'IY2R', 'IY0R', 'IY1L', 'IY2L', 'IY0L',
'IH1', 'IH2', 'IH0', 'IH1R', 'IH2R', 'IH0R', 'IH1L', 'IH2L', 'IH0L',
'EY1', 'EY2', 'EY0', 'EY1R', 'EY2R', 'EY0R', 'EY1L', 'EY2L', 'EY0L',
'EH1', 'EH2', 'EH0', 'EH1R', 'EH2R', 'EH0R', 'EH1L', 'EH2L', 'EH0L',
'AE1', 'AE2', 'AE0', 'AE1R', 'AE2R', 'AE0R', 'AE1L', 'AE2L', 'AE0L',
'AH1', 'AH2', 'AH0', 'AH1R', 'AH2R', 'AH0R', 'AH1L', 'AH2L', 'AH0L',
'ER1', 'ER2', 'ER0', 'R', 'ER1L', 'ER2L', 'ER0L',
'AY1', 'AY2', 'AY0', 'AY1R', 'AY2R', 'AY0R', 'AY1L', 'AY2L', 'AY0L',
'AA1', 'AA2', 'AA0', 'AA1R', 'AA2R', 'AA0R', 'AA1L', 'AA2L', 'AA0L',
'AW1', 'AW2', 'AW0', 'AW1R', 'AW2R', 'AW0R', 'AW1L', 'AW2L', 'AW0L',
'AO1', 'AO2', 'AO0', 'AO1R', 'AO2R', 'AO0R', 'AO1L', 'AO2L', 'AO0L',
'OY1', 'OY2', 'OY0', 'OY1R', 'OY2R', 'OY0R', 'OY1L', 'OY2L', 'OY0L',
'OW1', 'OW2', 'OW0', 'OW1R', 'OW2R', 'OW0R', 'OW1L', 'OW2L', 'OW0L',
'UH1', 'UH2', 'UH0', 'UH1R', 'UH2R', 'UH0R', 'UH1L', 'UH2L', 'UH0L',
'UW1', 'UW2', 'UW0', 'UW1R', 'UW2R', 'UW0R', 'UW1L', 'UW2L', 'UW0L',
'W', 'L')
p2fa_rl <- subset(p2fa_vplus, grepl('[RL]', p2fa_vplus))
##############################################################
# FUNCTIONS FOR PREPARING THE DATA
# findSubjectParameters
# findAllSubjectParameters
# normalizeFormants
# sortVowelFactor
# applyRule
# loadSegmentData
# loadFormantData
# loadSegmentFormantData
# trimParameter
##############################################################
findSubjectParameters <- function(m, formants=c('F1','F2','F3','F4','F5'), one.set=FALSE, default_meas=0.325,
params=c('frequency', 'log_bandwidth'), collapse.maxformant=FALSE, verbose=FALSE){
# Calculate means and standard devations for the purpose of normalizing vowel formants
#
# Args:
# m : a data frame containing unnormalized formant measurements
# formants : the formants to include in the normalization
#
# Returns a data frame containing formant means and standard devations for normalizing subjects' vowel spaces
#
if (!'subject'%in%names(m)){
m$subject <- 'subject'
}
if (!'measurement'%in%names(m)){
m$measurement <- default_meas
}
if (collapse.maxformant){
m$max_formant <- 0
}
fstart <- proc.time()
input_name <- paste(formants[1], params[1], sep='_')
Fmeans <- data.frame(subject = aggregate(m[,input_name] ~ m$subject * m$phone * m$measurement * m$max_formant, FUN=mean)[,1],
phone = aggregate(m[,input_name] ~ m$subject * m$phone * m$measurement * m$max_formant, FUN=mean)[,2],
measurement = aggregate(m[,input_name] ~ m$subject * m$phone * m$measurement * m$max_formant, FUN=mean)[,3],
max_formant = aggregate(m[,input_name] ~ m$subject * m$phone * m$measurement * m$max_formant, FUN=mean)[,4])
for (param in params){
for (i in 1:length(formants)){
input_names <- paste(formants, param, sep='_')
Fmeans_i <- aggregate(m[,input_names[i]] ~ m$subject * m$phone * m$measurement * m$max_formant, FUN=mean)
names(Fmeans_i) <- c('subject', 'phone', 'measurement', 'max_formant', paste(formants[i], param, sep='_'))
Fmeans <- merge(Fmeans, Fmeans_i, by=c('subject', 'phone', 'measurement', 'max_formant'))
}
}
Fnorm <- data.frame(subject = aggregate(Fmeans[,input_name] ~ Fmeans$subject*Fmeans$measurement*Fmeans$max_formant, FUN=mean)[,1],
measurement = aggregate(Fmeans[,input_name] ~ Fmeans$subject*Fmeans$measurement*Fmeans$max_formant, FUN=mean)[,2],
max_formant = aggregate(Fmeans[,input_name] ~ Fmeans$subject*Fmeans$measurement*Fmeans$max_formant, FUN=mean)[,3])
output_names <- c()
for (param in params){
mean_names <- c(paste(formants, param, 'mean', sep='_'))
sd_names <- c(paste(formants, param, 'sd', sep='_'))
output_names <- c(output_names, mean_names, sd_names)
input_names <- paste(formants, param, sep='_')
for (i in 1:length(formants)){
Fnorm[,mean_names[i]] = aggregate(Fmeans[,input_names[i]] ~ Fmeans$subject*Fmeans$measurement*Fmeans$max_formant, FUN=mean)[,4]
}
for (i in 1:length(formants)){
Fnorm[, sd_names [i]] = aggregate(Fmeans[,input_names[i]] ~ Fmeans$subject*Fmeans$measurement*Fmeans$max_formant, FUN=sd)[,4]
}
}
if (verbose){
print(paste('calculating parameter matrices for', length(unique(m$subject)), 'subjects,', length(unique(m$max_formant)),
'max formants, and', length(unique(m$measurement)), 'measurement points took', paste(round((proc.time()-fstart)[3],3)), 'seconds'))
}
if (one.set){
Fnorm[,output_names]
}else{
Fnorm
}
}
findAllSubjectParameters <- function(m, speakers=unique(m$subject), formants=c('F1','F2','F3','F4','F5'),
params=c('frequency', 'log_bandwidth'), collapse.maxformant=FALSE){
# Calculate means and standard devations for the purpose of normalizing vowel formants, for all speakers
#
# Args:
# m : a data frame, including unnormalized formant measurements
# speakers : the speakers to include
# formants : the formants to include in the normalization
# collapse.maxformant : whether to combine measurments taken with different max formant values
#
# Returns the parameters for the speakers.
parameters.by.speaker <- c()
for (speaker in speakers){
speaker.param <- findSubjectParameters(subset(m, subject==speaker), formants=formants, params=params,
collapse.maxformant=collapse.maxformant)
parameters.by.speaker <- rbind(parameters.by.speaker, speaker.param)
}
parameters.by.speaker
}
normalizeFormants <- function(m, pop_parameters, formant.param=NULL, formants=c('F1','F2','F3','F4','F5'), phones=levels(m$phone), best_maxformant=NULL, meas=7, collapse.maxformant=FALSE, params=c('frequency', 'log_bandwidth')){
# Transform a subject's vowel measurements using Lobanov normalization and the population's parameters
#
# Args:
# m : a data frame, including unnormalized formant measurements
# pop_parameters : formant means and standard deviations for the population
# formants : the formants to include in the normalization
# best_maxformant : a max formant value to use for the measurments to be normalized
#
# Returns the same data frame with added columns for subject means, subject standard deviations, and normalized formant frequencies.
#
if (is.null(formant.param)){
if (!'subject'%in%names(m)){
m$subject <- 'subject'
m$subject <- factor(m$subject)
}
if (is.null(best_maxformant)){
formant.param <- findSubjectParameters(m, formants=formants, collapse.maxformant=collapse.maxformant, params=params)
}else{
formant.param <- findSubjectParameters(subset(m, max_formant==best_maxformant&phone%in%phones), params=params, formants=formants,
collapse.maxformant=collapse.maxformant)
}
}
if (!'measurement'%in%names(formant.param)){
formant.param$measurement <- meas
}
for (param in params){
#print(param)
for (i in 1:length(formants)){
formant <- formants[i]
#print(formant)
input_freq <- m[,paste(formant, param, sep='_')]
subject_mean <- formant.param[formant.param$measurement==meas,paste(formant, param, 'mean', sep='_')]
subject_sd <- formant.param[formant.param$measurement==meas,paste(formant, param, 'sd', sep='_')]
pop_mean <- pop_parameters[,paste(formant, param, 'mean', sep='_')]
pop_sd <- pop_parameters[,paste(formant, param, 'sd', sep='_')]
#print (length(input_freq))
#print (length(subject_mean))
#print (subject_sd)
#print (pop_sd)
#print (pop_mean)
m[,paste(formant, param, sep='n_')] <- ((input_freq - subject_mean) / subject_sd) * pop_sd + pop_mean
}
}
m
}
sortVowelFactor <- function(phone_factor, add_missing=TRUE, vowels_sorted=NULL, language='French'){
# Sort the factor levels to put vowels in order.
#
# Args:
# phone_factor : a factor whose levels are vowels
# add_missing : add levels for vowels that aren't present
# vowels_sorted : a list of vowel labels in the desired order
#
# Returns phone_factor with its levels in the same order as vowels_sorted
#
if (is.null(vowels_sorted)){
if (language=='French'){
vowels_sorted <- c('i','iC','y','yC','e','ø','ɛ','ɛ̃','ɛʁ','œ','œ̃','ə','a','ɑ̃','ɔ̃','ɔ','o','uC','u')
print ('using French vowel transcriptions')
}else if (language=='English'){
#if ('ih'%in%levels(phone_factor)){
# vowels_sorted <- buckeye_vowels
# print ('using Buckeye Corpus vowel transcriptions')
#}else{
# vowels_sorted <- p2fa_vowels
# print ('using P2FA vowel transcriptions')
#}
if (is.null(vowels_sorted)){
vowels_sorted <- p2fa_vowels
}
}
}
extra_levels <- setdiff(levels(phone_factor), vowels_sorted)
missing_levels <- setdiff(vowels_sorted, levels(phone_factor))
if (length(missing_levels)){
if (add_missing){
levels(phone_factor) <- c(levels(phone_factor), missing_levels)
print(paste('added levels not in the data:', paste(missing_levels, collapse=' ')))
}else{
print(paste('expected levels are missing:', paste(missing_levels, collapse=' ')))
}
}
for (p in rev(vowels_sorted)){
if (p%in%levels(phone_factor)){
phone_factor <- relevel(phone_factor, p)
}
}
phone_factor
}
applyRule <- function(data, rule_id, language='French'){
# Apply a phonological rule to derive phones not distinguished in the TextGrid
#
# Args:
# data : a data frame containing phones and their contexts
# rule_id : a string indicating which rule to apply
# language : the language in the recording
#
# Returns the same data frame with updated phone labels
#
print (paste('applying', rule_id, 'for', language))
if (language=='French'){
if (rule_id=='laxing'){
# HIGH VOWEL LAXING
laxing <- c()
for (i in 1:(length(data$phone)-2)){
if (data$phone[i]%in%high_vowels){
if (data$phone[i+1]%in%laxing_triggers & data$phone[i+2]==word_boundary){
laxing <- c(laxing, paste(data$phone[i], 'C', sep=''))
}else if (data$phone[i+1]%in%consonants_not_s_or_r & data$phone[i+2]%in%consonants_not_approx){
laxing <- c(laxing, paste(data$phone[i], 'C', sep=''))
}else{
laxing <- c(laxing, paste(data$phone[i]))
}
}else{
laxing <- c(laxing, paste(data$phone[i]))
}
}
laxing <- c(laxing, paste(data$phone[length(data$phone)-c(1,0)]))
data$phone <- factor(laxing)
}
if (rule_id=='diphthongization'){
# DIPHTHONGIZATION OF PRE-RHOTIC /ɛ/
diphthongizing <- c()
for (i in 1:(length(data$phone)-1)){
if (data$phone[i]=='ɛ' & data$phone[i+1]=='ʁ'){
diphthongizing <- c(diphthongizing, paste('ɛʁ'))
}else{
diphthongizing <- c(diphthongizing, paste(data$phone[i]))
}
}
diphthongizing <- c(diphthongizing, paste(data$phone[length(data$phone)]))
data$phone <- factor(diphthongizing)
}
}
if (language=='English'){
if (rule_id=='u_fronting'){
# FRONTING OF PRE-CORONAL /t/
fronting <- c(paste(data$phone[1]))
for (i in 2:(length(data$phone))){
if (substr(data$phone[i],1,2)=='UW'){
if (data$phone[i+1]%in%p2fa_coronals){
fronting <- c(fronting, paste('cor',data$phone[i], sep=''))
}else{
fronting <- c(fronting, paste(data$phone[i]))
}
}else{
fronting <- c(fronting, paste(data$phone[i]))
}
}
data$phone <- factor(fronting)
}
if (rule_id=='prerhotic_vowels'){
# QUALITY OF PRE-RHOTIC VOWELS
prerhotic_vowels <- c()
for (i in 1:(length(data$phone)-1)){
if (data$phone[i+1]%in%c('R','r')){
#IY,IH --> IR / __R
if (substr(data$phone[i],1,1)=='I'){
prerhotic_vowels <- c(prerhotic_vowels, paste('IR',substr(data$phone[i],3,3), sep=''))
}else if (substr(data$phone[i],1,1)=='i'){
prerhotic_vowels <- c(prerhotic_vowels, 'ir')
#UH,UW --> UR / __R
}else if (substr(data$phone[i],1,1)=='U'){
prerhotic_vowels <- c(prerhotic_vowels, paste('UR',substr(data$phone[i],3,3), sep=''))
}else if (substr(data$phone[i],1,1)=='u'){
prerhotic_vowels <- c(prerhotic_vowels, 'ur')
#EY --> EYR / __R
#}else if (substr(data$phone[i],1,2)=='EY'){
# prerhotic_vowels <- c(prerhotic_vowels, paste('EYR',substr(data$phone[i],3,3), sep=''))
#AA --> AAR / __R
#}else if (substr(data$phone[i],1,2)=='AA'){
# prerhotic_vowels <- c(prerhotic_vowels, paste('AAR',substr(data$phone[i],3,3), sep=''))
#AO,OW --> OR / __R
}else if (substr(data$phone[i],1,2)%in%c('AO','OW')){
prerhotic_vowels <- c(prerhotic_vowels, paste('OR',substr(data$phone[i],3,3), sep=''))
}else if (substr(data$phone[i],1,1)%in%c('ao','ow')){
prerhotic_vowels <- c(prerhotic_vowels, 'or')
}else if (data$phone[i]%in%buckeye_vowels){
prerhotic_vowels <- c(prerhotic_vowels, paste(substr(data$phone[i],1,2), 'r', sep=''))
}else{
prerhotic_vowels <- c(prerhotic_vowels, paste(substr(data$phone[i],1,2), 'R',substr(data$phone[i],3,3), sep=''))
}
}else{
prerhotic_vowels <- c(prerhotic_vowels, paste(data$phone[i]))
}
}
prerhotic_vowels <- c(prerhotic_vowels, paste(data$phone[length(data$phone)]))
data$phone <- factor(prerhotic_vowels)
}
if (rule_id=='prelateral_vowels'){
# QUALITY OF PRE-LATERAL VOWELS
prelateral_vowels <- c()
for (i in 1:(length(data$phone)-1)){
#V --> VL / __L
if (data$phone[i+1]=='L'){
prelateral_vowels <- c(prelateral_vowels, paste(substr(data$phone[i],1,2), 'L',substr(data$phone[i],3,3), sep=''))
}else if (data$phone[i+1]=='l'){
prelateral_vowels <- c(prelateral_vowels, paste(substr(data$phone[i],1,2), 'l', sep=''))
}else{
prelateral_vowels <- c(prelateral_vowels, paste(data$phone[i]))
}
}
prelateral_vowels <- c(prelateral_vowels, paste(data$phone[length(data$phone)]))
data$phone <- factor(prelateral_vowels)
}
if (rule_id=='prenasal_vowels'){
# QUALITY OF PRE-NASAL VOWELS
prenasal_vowels <- c()
for (i in 1:(length(data$phone)-1)){
#V --> VN / __N
if (data$phone[i+1]%in%c('M','N','NG')){
prenasal_vowels <- c(prenasal_vowels, paste(substr(data$phone[i],1,2), 'N',substr(data$phone[i],3,3), sep=''))
}else if (data$phone[i+1]=='l'){
prenasal_vowels <- c(prenasal_vowels, paste(substr(data$phone[i],1,2), 'n', sep=''))
}else{
prenasal_vowels <- c(prenasal_vowels, paste(data$phone[i]))
}
}
prenasal_vowels <- c(prenasal_vowels, paste(data$phone[length(data$phone)]))
data$phone <- factor(prenasal_vowels)
}
}
data
}
loadSegmentData <- function(filename, language='French', min_duration=0.05, exclude_nonvowels=TRUE, applyrules=TRUE, target_phones=NULL, merge_vl=FALSE){
# Load the vowel segment information output by formants_first_pass.praat
#
# Args:
# s : the data
# language : the language in the recording
# min_duration : the minimum duration for a segment
# Returns a data frame containing the data from the file and some new columns
#
data <- read.table(filename, h=T, sep='\t', comment.char='', na.strings='--undefined--')
if (language=='French'){
#THIS IS NOT THE BEST PLACE TO DO THESE...
#o_words <- c('aucun', 'auteur', 'chaud', 'chauffeur', 'château', 'dos', 'faux', 'lot', 'métaux', 'mot', 'taux')
#if (!'o'%in%levels(data$phone)){
# levels(data$phone) <- c(levels(data$phone), 'o')
#}
#data[data$word%in%o_words&data$phone=='ɔ',]$phone <- 'o'
#wrong_spelling <- c('delit','depanneur','la','pecheurs','spêcheurs','vipere')
#right_spelling <- c('délit','dépanneur','là','pêcheurs','pêcheurs','vipère')
#for (i in 1:length(wrong_spelling)){
# levels(data$word)[which(levels(data$word)==wrong_spelling[i])] <- right_spelling[i]
#}
#APPLY PHONOLOGICAL RULES
if (applyrules){
data <- applyRule(data, 'laxing', language=language)
data <- applyRule(data, 'diphthongization', language=language)
}
#SORT THE VOWELS AND ADD MISSING LEVELS
data$phone <- sortVowelFactor(data$phone, add_missing=TRUE, target_phones, language)
}else if (language=='English'){
#APPLY PHONOLOGICAL RULES
if (applyrules){
data <- applyRule(data, 'prerhotic_vowels', language=language)
data <- applyRule(data, 'prelateral_vowels', language=language)
data <- applyRule(data, 'prenasal_vowels', language=language)
}
#SORT THE VOWELS AND ADD MISSING LEVELS
data$phone <- sortVowelFactor(data$phone, add_missing=TRUE, target_phones, language)
}
measured.data <- subset(data, measured==1)
if (sum(measured.data$duration<min_duration)>0){
print(paste(round(100*sum(measured.data$duration<min_duration)/length(measured.data$duration), 2), '% of tokens will be excluded (under duration threshold).', sep=''))
}
data <- add.contexts(data)
#MERGE VOWEL-LIQUID SEQUENCES, IF ASKED TO
if (merge_vl){
print ('merging vowel-liquid sequences')
data <- mergeVL(data)
}
#EXCLUDE OVERLY SHORT TOKENS
data$duration <- data$phoneend - data$phonestart
data$exclude <- as.numeric(data$duration<min_duration)
# print ('data$exclude...')
# print (summary(data))
#EXCLUDE TAGS IN THE WORDS TIER
if (sum(substr(data$word,1,1)=='<')>0){
data[substr(data$word,1,1)=='<',]$exclude <- 1
}
#EXCLUDE NON-VOWELS
if (exclude_nonvowels){
#print(setdiff(unique(data$phone),all_vowels))
if (length(setdiff(unique(data$phone),all_vowels))){
#print(summary(data[!data$phone%in%all_vowels,]))
data[!data$phone%in%all_vowels,]$exclude <- 1
}
}
data
}
loadFormantData <- function(filename, segment.data=c(), nasal_formant='no', language=NULL, formants=c('F1','F2','F3','F4','F5'), default_meas=NA,meas_override=TRUE){
# Load the formant measurements output by formants_first_pass.praat
#
# Args:
# s : the data
# nasal_formant : 'yes' to use nasal vowel measurements taking into account nasal F1,
# 'no' to use normal measurements for nasal vowels, or 'exclude' to exclude nasal vowels
# language : the language in the recording
#
# Returns a data frame containing the data from the file and some new columns
#
data <- read.table(filename, h=T, sep='\t', comment.char='', na.strings='--undefined--')
#print (summary(segment.data))
if (meas_override | (nrow(data)==length(default_meas)*nrow(unique(data[,c('token_id','max_formant','nasal_formant')])))){
data$measurement <- default_meas
}else{
data$measurement <- NA
}
#EXCLUDE OVERLY SHORT TOKENS
if (sum(data$token_id%in%subset(segment.data, exclude==1)$token_id) > 0){
print(paste('excluding ', round(100*sum(data$token_id%in%subset(segment.data, exclude==1)$token_id)/length(data$token_id), 2), '% of tokens.', sep=''))
}
data <- data[data$token_id%in%subset(segment.data, exclude==0)$token_id,]
#DEAL WITH NASALIZED VOWELS
nasal_tokens <- intersect(unique(subset(data, nasal_formant==0)$token_id), unique(subset(data, nasal_formant==1)$token_id))
if (nasal_formant=='no'){
data <- subset(data, nasal_formant==0|!token_id%in%nasal_tokens)
} else if (nasal_formant=='yes'){
data <- subset(data, nasal_formant==1|!token_id%in%nasal_tokens)
} else if (nasal_formant=='exclude'){
data <- subset(data, !token_id%in%nasal_tokens)
}
#CALCULATE LOG BANDWITHS
for (formant in formants){
data[,paste(formant,'log_bandwidth',sep='_')] <- log(data[,paste(formant,'bandwidth',sep='_')], base=10)
}
data
}
loadSegmentFormantData <- function(datafiles, s, language='English', contours=FALSE, default_meas=NA, exclude_nonvowels=TRUE, applyrules=TRUE, min_duration=0.05, target_phones=NULL, merge_vl=FALSE){
segment.data <- c()
formant.data <- c()
for (datafile in subset(datafiles, speaker==s & exclude==0)$filepath){
#LOAD THE SEGMENT AND FORMANT FILES PRODUCED BY THE PRAAT SCRIPT
print (paste('loading',datafile))
segment.part <- loadSegmentData(paste(datapath,datafile,'_segments.csv',sep=''), language=language, exclude_nonvowels=exclude_nonvowels, applyrules=applyrules, min_duration=min_duration, target_phones=target_phones, merge_vl=merge_vl)
segment.part$textgrid <- datafile
if (contours){
formant.part <- loadFormantData(paste(datapath,datafile,'_formant_contours.csv',sep=''),
segment.data=segment.part, language=language, default_meas=default_meas)
instruct.part <- read.table(paste(datapath,datafile,'_contour_instructions.csv',sep=''), h=T, sep='\t')
segment.part <- merge(segment.part[,setdiff(names(segment.part),c('phonestart','phoneend','duration'))],
instruct.part[,c('token_id','mdist','phonestart','phoneend')], by='token_id')
segment.part$duration <- with(segment.part, phoneend-phonestart)
}else{
segment.part <- subset(segment.part, measured==1)
formant.part <- loadFormantData(paste(readpath,datafile,'_formant_nuclei.csv',sep=''),
segment.data=segment.part, language=language, default_meas=default_meas)
}
#THIS IS TO OMIT DUPLICATES CAUSED BY OVERLAPPING TEXTGRIDS (FIRST APPEARANCE IN LIST SHOULD BE DEFINITIVE VERSION).
unique_ids <- length(unique(formant.part$token_id))
segment.part <- subset(segment.part, !token_id%in%unique(segment.data$token_id))
formant.part <- subset(formant.part, !token_id%in%unique(formant.data$token_id))
if (unique_ids > length(unique(formant.part$token_id))){
print (paste('excluding', unique_ids-length(unique(formant.part$token_id)), 'tokens whose IDs were already present.'))
}
segment.data <- rbind(segment.data, segment.part)
formant.data <- rbind(formant.data, formant.part)
#print ('SEGMENT ROWS')
#print (nrow(segment.data))
}
list(segments=segment.data, formants=formant.data)
}
trimParameter <- function(m, params, bywhat='subject', sds=2, tails=c(-1,1)){
# Trim values that are more than some number of standard deviations from the mean
#
# Args:
# m : the data
# params : the parameters to use for trimming
# bywhat : the grouping variable to use to find standard deviations
# sds : the number of standard deviations to use as the cutoff
# tail : which side(s) to trim (c(1), c(-1), or c(-1,1))
#
# Returns the same data table without the extreme values
#
for (param in params){
for (tail in tails){
if (length(bywhat)==1){
cutoffs <- data.frame(bywhat=aggregate(m[,param]~m[,bywhat[1]], FUN=mean)[,1],
cutoff=aggregate(m[,param]~m[,bywhat[1]], FUN=mean)[,2]
+ tail*sds*aggregate(m[,param]~m[,bywhat[1]], FUN=sd)[,2])
}else{
cutoffs <- data.frame(bywhat1=aggregate(m[,param]~m[,bywhat[1]]*m[,bywhat[2]], FUN=mean)[,1],
bywhat2=aggregate(m[,param]~m[,bywhat[1]]*m[,bywhat[2]], FUN=mean)[,2],
cutoff=aggregate(m[,param]~m[,bywhat[1]]*m[,bywhat[2]], FUN=mean)[,3]
+ tail*sds*aggregate(m[,param]~m[,bywhat[1]]*m[,bywhat[2]], FUN=sd)[,3])
}
names(cutoffs)[1:length(bywhat)] <- bywhat
mc <- merge(m, cutoffs, by=intersect(names(m), names(cutoffs)))
if (tail > 0){
mc <- mc[mc[,param]<mc$cutoff,]
}else{
mc <- mc[mc[,param]>mc$cutoff,]
}
m <- mc[,1:length(names(mc))-1]
}
}
m
}
##############################################################
# PLOTTING FUNCTIONS
# plot.vowels
# plotVowelBands
##############################################################
plot.vowels <- function(m, param1, param2, group.by='phone', meas=NULL, mf=NULL, limits='global', size_param=FALSE, inches=FALSE, manual.xlim=FALSE, manual.ylim=FALSE, xlab=NULL, ylab=NULL, do.ellipses=TRUE, fill=FALSE, do.text=TRUE, noplot=FALSE, subject=NULL, main='', draw.zero=TRUE, replace.underscores=TRUE, rename.axes=TRUE, levels.to.highlight=NULL, label.big.circles=NULL, pch=19, col.override=NULL, overplot=FALSE, rainbow_multiplier=1.2, plot_points=TRUE){
# Plot vowel tokens on a single plot
#
# Args:
# m : the data
# meas : the measurement point
# param1 : the parameter to plot on the x-axis
# param2 : the parameter to plot on the y-axis
# limits : if 'global', use the same axis limits for all speakers
# size_param : the parameter that will determine the size of the plot symbols. If FALSE, plot with points instead
# inches : controls the size of the symbols
# manual.xlim : manual x-axis limits
# manual.ylim : manual y-axis limits
# do.ellipses : whether to include vowel category ellipses in the plot
# noplot : don't plot
#
# Returns nothing
#
#
# plot.vowels <- function(data, 'LD1', 'LD2', group.by='phone')
#
if (is.null(meas)&is.null(mf)){
afsub <- m
xlim <- rev(range(afsub[,param1]))
ylim <- rev(range(afsub[,param2]))
}else{
if (!'measurement'%in%names(m)){
m$measurement <- meas[1]
}
if (meas=='all'){
meas <- unique(m$measurement)
}
if (mf[1]=='all'){
afsub <- subset(m, measurement%in%meas)
}else{
if (mf[1]=='best'){
afsub <- subset(m, measurement%in%meas&max_formant==best_max)
}else{
afsub <- subset(m, measurement%in%meas&max_formant%in%mf)
}
}
afsub <- subset(afsub, !is.na(afsub[,param1]))
afsub <- subset(afsub, !is.na(afsub[,param2]))
if (limits=='global'){
xlim <- rev(range(na.omit(subset(m, measurement%in%meas)[,param1])))
ylim <- rev(range(na.omit(subset(m, measurement%in%meas)[,param2])))
}else{
xlim <- rev(range(afsub[,param1]))
ylim <- rev(range(afsub[,param2]))
}
if (main==''){
if (length(meas)>1){
main <- subject
}else{
if (length(mf)==1){
main <- paste(subject, ', ', mf, ' Hz, ', meas, sep='')
}else{
main <- paste(meas, sep='')
}
}
}
}
if (manual.xlim[1]!=FALSE){
xlim <- manual.xlim
}
if (manual.ylim[1]!=FALSE){
ylim <- manual.ylim
}
if (is.null(subject) & !is.null(afsub$subject)){
subject <- afsub$subject[1]
}
if(is.null(xlab)){
xlab <- param1
if (rename.axes){
xlab <- gsub('F1n_', 'normalized F1 ', xlab)
xlab <- gsub('F2n_', 'normalized F2 ', xlab)
xlab <- gsub('F3n_', 'normalized F3 ', xlab)
xlab <- gsub('F4n_', 'normalized F4 ', xlab)
xlab <- gsub('F5n_', 'normalized F5 ', xlab)
xlab <- gsub('frequency', 'frequency (Hz)', xlab)
}
if (replace.underscores){
xlab <- gsub('_', ' ', xlab)
}
}
if(is.null(ylab)){
ylab <- param2
if (rename.axes){
ylab <- gsub('F1n_', 'normalized F1 ', ylab)
ylab <- gsub('F2n_', 'normalized F2 ', ylab)
ylab <- gsub('F3n_', 'normalized F3 ', ylab)
ylab <- gsub('F4n_', 'normalized F4 ', ylab)
ylab <- gsub('F5n_', 'normalized F5 ', ylab)
ylab <- gsub('frequency', 'frequency (Hz)', ylab)
}
if (replace.underscores){
ylab <- gsub('_', ' ', ylab)
}
}
if (noplot){
plot(0, 0, type='n', xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab, main=main)
}else{
if (size_param==FALSE){
if (!overplot){
plot(0, 0, xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab, type='n', main=main)
}
if (draw.zero){
abline(h=0, col='gray')
abline(v=0, col='gray')
}
if (is.null(levels.to.highlight)){
if (is.null(col.override)){
colors <- rainbow(rainbow_multiplier*length(levels(afsub[,group.by])), s=1, v=1)
}else{
colors <- col.override
}
if (plot_points){
points(afsub[,param1], afsub[,param2], pch=pch, cex=0.6, col=colors[c(afsub[,group.by])])
}
}else{
afsubsub <- afsub[afsub[,group.by]%in%levels.to.highlight,]
afnotsub <- afsub[!afsub[,group.by]%in%levels.to.highlight,]
if (is.null(col.override)){
colors <- rainbow(rainbow_multiplier*length(levels(afsub[,group.by])), s=0.1, v=0.9)
}else{
colors <- col.override
}
if (plot_points){
points(afnotsub[,param1], afnotsub[,param2], pch=pch, cex=0.6, col=colors[c(afnotsub[,group.by])])
}
if (is.null(col.override)){
colors <- rainbow(rainbow_multiplier*length(levels(afsub[,group.by])), s=1, v=1)
}else{
colors <- col.override
}
if (plot_points){
points(afsubsub[,param1], afsubsub[,param2], pch=pch, cex=0.6, col=colors[c(afsubsub[,group.by])])
}
}
}else{
symbols(afsub[,param1], afsub[,param2], circles=afsub[,size_param], inches=inches,
xlim=xlim, ylim=ylim,
xlab=xlab, ylab=ylab,
bg=rainbow(rainbow_multiplier*length(levels(afsub[,group.by])))[c(afsub[,group.by])],
main=main)
if (!is.null(label.big.circles)){
big.circles <- subset(afsub, afsub[,size_param]>label.big.circles)
text(big.circles[,param1], big.circles[,param2], labels=round(big.circles[,size_param],0), cex=0.5)
}
}
}
if (is.null(levels.to.highlight)){
colors <- rainbow(rainbow_multiplier*length(levels(afsub[,group.by])), v=1, alpha=0.5)
}else{
colors <- rainbow(rainbow_multiplier*length(levels(afsub[,group.by])), v=0.85, alpha=c(0,1)[1+as.numeric(levels(afsub[,group.by])%in%levels.to.highlight)])
}
for (i in 1:length(levels(afsub[,group.by]))){
p <- levels(afsub[,group.by])[i]
if (length(subset(afsub, afsub[,group.by]==p)[,1])>2 & do.ellipses){
dataEllipse(c(subset(afsub, afsub[,group.by]==p)[,param1]), c(subset(afsub, afsub[,group.by]==p)[,param2]),
center.pch=F, plot.points=F, levels=.682, add=TRUE, segments=51, robust=FALSE,
col=colors[i], fill=fill)
#}else{
# print(paste('no ellipse for',p))
}
}
if (do.text){
if (is.null(levels.to.highlight)){
colors <- rainbow(rainbow_multiplier*length(levels(afsub[,group.by])), s=0.9, v=0)
}else{
colors <- rainbow(rainbow_multiplier*length(levels(afsub[,group.by])),
v=c(.6,0)[1+as.numeric(levels(afsub[,group.by])%in%levels.to.highlight)],
alpha=c(1,1)[1+as.numeric(levels(afsub[,group.by])%in%levels.to.highlight)],
s=c(.4,1)[1+as.numeric(levels(afsub[,group.by])%in%levels.to.highlight)])
}
text(aggregate(afsub[,param1] ~ afsub[,group.by], FUN=mean)[,2],
aggregate(afsub[,param2] ~ afsub[,group.by], FUN=mean)[,2],
aggregate(afsub[,param1] ~ afsub[,group.by], FUN=mean)[,1], cex=1, col=colors)
}
}
plotVowelBands <- function(data, plot.lines='n_frequency', plot.bands='_bandwidth', formants=c('F1','F2','F3','F4','F5'),
main='', xlab='', ylab='', alpha=0.3, v=1, smoothing=1000, kHz=TRUE){
# Plot vowel frequencies and bandwidths over time
#
# Args:
# data : the data
# plot.lines : a string to append to formant names in order to derive the column name for what to plot using lines
# plot.bands : a string to append to formant names in order to derive the column name for what to plot using bands
# formants : the formants to include in the plot
# main : the main title
# xlab : the x-axis label
# ylab : the y-axis label
# alpha : the alpha value for the band fill color
# v : the value for the line color and band fill color
# smoothing : if FALSE, plot the actual values, if a number, smooth by interpolating to that many values
# kHz : if TRUE, label the y-axis in kHz instead of Hz
#
# Returns nothing
#
for (p in levels(data$phone)){
pdata <- subset(data, phone==p)
pct <- (pdata$measurement-0.5) / max(pdata$measurement)
topfreq <- data[,paste(formants[length(formants)], plot.lines, sep='')]
topband <- data[,paste(formants[length(formants)], plot.bands, sep='')]
if (kHz){
topfreq <- topfreq / 1000
topband <- topband / 1000
}
plot(0, 0, xlim=c(0,1), ylim=c(0, max(topfreq+topband)), type='n', xlab=xlab, ylab=ylab, main=paste('/',p,'/ ',main,sep=''))
lf <- length(formants)
if (smoothing){
plot.x <- seq(min(pct), max(pct), length.out=smoothing)
}else{
plot.x <- pct
}
for (i in 1:lf){
formant = formants[i]
freq <- pdata[,paste(formant, plot.lines, sep='')]
band <- pdata[,paste(formant, plot.bands, sep='')]/2
if (kHz){
freq <- freq / 1000
band <- band / 1000
}
if (smoothing){
f.freq <- approxfun(pct, freq)
freq <- f.freq(plot.x)
f.band <- approxfun(pct, band)
band <- f.band(plot.x)
}
polygon(c(plot.x, rev(plot.x)), c(freq+band, rev(freq-band)), col=rainbow(rainbow_multiplier*lf, alpha=alpha, v=v)[i], border=F)
lines(plot.x, freq, col=rainbow(rainbow_multiplier*lf, v=v)[i], lwd=2)
}
}
}
##############################################################
# FUNCTIONS FOR SELECTING FORMANT MEASUREMENTS
# readCovarMatrices
# readMeans
# findPopParameters
# findVowelMeans
# findCovarianceMatrices
# measurement.columns
# findSpanMiddle
# find.best.measurements
##############################################################
readCovarMatrices <- function(filename, language='French', phones=NULL){
# Read the formant and log bandwidth covariance matrices from a table and put them in a list.
#
# Args:
# filename : the filename (for a file containing the table)
#
# Returns a list of covariance matrices (one matrix per vowel)
#
all.covar <- list()
all.covar.table = read.table(filename, h=T, sep='\t')
all.covar.table$phone <- sortVowelFactor(all.covar.table$phone, language=language)
if (language=='English'){
#all.covar.table <- subset(all.covar.table, phone%in%c(p2fa_vowels,buckeye_vowels))
all.covar.table <- subset(all.covar.table, phone%in%c(target_phones))
all.covar.table$phone <- factor(all.covar.table$phone)
}else{
all.covar.table <- subset(all.covar.table, phone%in%c(french_vowels_sorted))
all.covar.table$phone <- factor(all.covar.table$phone)
}
covar.names <- names(all.covar.table)[2:length(all.covar.table[1,])]
if (is.null(phones)){
phones <- levels(all.covar.table$phone)
}
for (p in levels(all.covar.table$phone)){
if (p%in%phones){
all.covar[[p]] <- as.matrix(subset(all.covar.table, phone==p)[,covar.names], dimnames=list(covar.names, covar.names))
rownames(all.covar[[p]]) <- colnames(all.covar[[p]])
}
}
all.covar
}
readMeans <- function(filename, target_phones, language){
all.means <- read.table(filename, h=T, sep='\t')
all.means <- subset(all.means, phone%in%target_phones)
all.means$phone <- factor(all.means$phone)
all.means$phone <- sortVowelFactor(all.means$phone, language=language, vowels_sorted=target_phones)
all.means <- all.means[with(all.means, order(phone, measurement)),]
all.means
}
findPopParameters <- function(data, formants=c('F1','F2','F3','F4','F5'), write.to.file=FALSE, filename, params=c('frequency', 'log_bandwidth'), old_parameters=NULL, pass_id=NULL){
# Find the mean and standard deviation of the vowel category means for each formant,
# in order to transform Lobanov normalized vowels back into Hertz.
#
# Args:
# data : a data table including unnormalized formant frequencies
# formants : the formants to include in the table
# write.to.file : whether to write the result to a file
# filename : the filename to use (if writing to a file)
#
# Returns a data frame with the means and standard deviations
#
population <- data.frame(group='all')
for (param in params){
col_names <- c(paste(formants, param, 'mean', sep='_'), paste(formants, param, 'sd', sep='_'))
for (p in col_names){
population[,p] <- mean(aggregate(data[,p] ~ data[,'subject'], FUN=mean)[,2])
}
}
if (write.to.file==TRUE){
write_fn <- paste(writepath,filename,'.csv',sep='')
write.table(population, file=write_fn, row.names=FALSE, sep='\t')
print(paste('wrote',write_fn))
if (!is.null(pass_id)){
write_fn <- paste(writepath,filename,'_',pass_id,'.csv',sep='')
write.table(population, file=write_fn, row.names=FALSE, sep='\t')
print(paste('wrote',write_fn))
}
}
if (!is.null(old_parameters)){
if (ncol(population) == ncol(old_parameters) & nrow(population) == nrow(old_parameters)){
print('Change in population parameters:')
print(cbind(population[,1],population[,2:ncol(population)]-old_parameters[,2:ncol(population)]))
}else{
print('(Not comparing with old population parameters because the data frames are not the same size)')
}
print('(Not comparing with old population parameters because there aren\'t any)')
}
population
}
findVowelMeans <- function(data, formants=c('F1','F2','F3','F4','F5'), write.to.file=FALSE, filename, language='French', default_meas=0.325, old_vowel_means=NULL, normalized=TRUE, pass_id=NULL, target_phones=NULL){
# Find the mean formant frequency and log bandwidth for each vowel.
#
# Args:
# data : a data table including normalized formant frequencies and log bandwidths
# formants : the formants to include in the table
# write.to.file : whether to write the result to a file
# filename : the filename to use (if writing to a file)
#
# Returns a data frame with the mean frequencies and log bandwidths
#
if (!'measurement'%in%names(data)){
data$measurement <- default_meas
}
if (normalized){
sep <- 'n_'
}else{
sep <- '_'
}
vowel_means <- aggregate(data[,paste(formants[1], 'frequency', sep=sep)] ~ data[,'measurement'] * data[,'phone'], FUN=mean)
Fcol <- paste(formants[1], 'frequency', sep=sep)
names(vowel_means) <- c('measurement', 'phone', Fcol)
vowel_means$phone <- sortVowelFactor(vowel_means$phone, language=language, vowels_sorted=target_phones)
vowel_means <- vowel_means[with(vowel_means, order(phone, measurement)),]
if (length(formants)>1){
for (formant in formants[2:length(formants)]){
Fcol <- paste(formant, 'frequency', sep=sep)
new_column <- aggregate(data[,Fcol] ~ data[,'measurement'] * data[,'phone'], FUN=mean)
names(new_column) <- c('measurement', 'phone', Fcol)
vowel_means <- merge(vowel_means, new_column, by=c('measurement','phone'))
}
}
for (formant in formants){
Fcol <- paste(formant, 'log_bandwidth', sep=sep)
new_column <- aggregate(data[,Fcol] ~ data[,'measurement'] * data[,'phone'], FUN=mean)